CNN is one of the algorithms in Deep learning that automatically captures and identifies the important features without the need of human supervision[1]}. Local connections and shared weights employed in the CNN are useful in extracting features from 2-D input signals such as image signals. Basically, CNN has three kinds of layers: convolution layer, pooling layer and fully connected layer. Convolution layer is primarily associated with the identification of features from raw data. This is achieved by applying filters having predefined size followed by convolution operation. Pooling layer applies a pooling operation that reduces the dimension of feature maps while retaining the important features[2]}. Some of the pooling methods are max pooling and average pooling. The fully connected layer or the dense layer generates forecasting after features extracting process. The final fully connected layers have flattened features arising after applying convolution and pooling operations[3]}, [4]}.
